{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 14\n",
    "# Trees and Forests\n",
    "\n",
    "The basis of tree-based learners is the decision tree wherein a series of decision rules are chained. \n",
    "* https://www.stat.berkeley.edu/~breiman/RandomForests/\n",
    "\n",
    "## 14.1 Training a Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the prediction for [[5, 4, 3, 2]] is [1]\n",
      "predicted probabilities for the three classes: [1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "targets = iris.target\n",
    "\n",
    "decisiontree = DecisionTreeClassifier(random_state=0)\n",
    "model = decisiontree.fit(features, targets)\n",
    "\n",
    "observation = [[5, 4, 3, 2]]\n",
    "print(\"the prediction for {} is {}\".format(observation, model.predict(observation)))\n",
    "print(\"predicted probabilities for the three classes: {}\".format(model.predict(observation)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Decision tree learners attempt to find a decision rule that produces the greatest decrease in impurity at a node. While there are a number of measurements of impurity, by default `DecisionTreeClassifier` uses Gini impurity:\n",
    "$$\n",
    "G(t) = 1 - \\sum_{i=1}^c{p_i^2}\n",
    "$$\n",
    "where G(t) is the Gini impurity at node t and $p_i$ is the proportion of observations of class c at node t.\n",
    "\n",
    "This process of finding the decision rules that create splits to increase impurity is repeated recursively untill all leaf nodes are pure (i.e. contain only one class) or some abritary cut-off is reached\n",
    "\n",
    "We can change the `criterion` parameter to use a different impurity measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create decision tree classifier using entropy\n",
    "decisiontree_entropy = DecisionTreeClassifier(criterion='entropy', random_state=0)\n",
    "\n",
    "model_entropy = decisiontree_entropy.fit(features, targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "* Decision Tree Learning, Princeton (https://www.cs.princeton.edu/courses/archive/spr07/cos424/papers/mitchell-dectrees.pdf)\n",
    "\n",
    "## Training a Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston()\n",
    "features = boston.data[:,0:2]\n",
    "target = boston.target\n",
    "\n",
    "decisiontree = DecisionTreeRegressor(random_state=0)\n",
    "model = decisiontree.fit(features, target)\n",
    "\n",
    "observation = [[0.02, 16]]\n",
    "model.predict(observation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "Decision tree regression works similarly to decision tree classification, however instead of reducing Gini impurity or entropy, potential splits are by default measure on how much they reduce mean squared error (MSE):\n",
    "$$\n",
    "MSE = \\frac{1}{n} \\sum_{i=1}^{n}{(y_i - \\hat y_i)^2}\n",
    "$$\n",
    "\n",
    "where $y_i$ is the true value of the target and $\\hat y_i$ is the predicted value.\n",
    "\n",
    "We can use the `criterion` parameter to select the desired measurement of split quality. For example we can construct a tree whose splits reduce mean absolute error:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "decisiontree_mae = DecisionTreeRegressor(criterion=\"mae\", random_state=0)\n",
    "model_mae = decisiontree_mae.fit(features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "* Decision Tree Regression, scikit-learn (http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html)\n",
    "* http://saedsayad.com/decision_tree_reg.htm\n",
    "\n",
    "## 14.3 Visualizing a Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pydotplus\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "from IPython.display import Image\n",
    "from sklearn import tree\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "decisiontree = DecisionTreeClassifier(random_state=0)\n",
    "\n",
    "model = decisiontree.fit(features, target)\n",
    "\n",
    "dot_data = tree.export_graphviz(decisiontree, out_file=None, feature_names=iris.feature_names, class_names=iris.target_names)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "Image(graph.create_png())\n",
    "\n",
    "# create pdf\n",
    "#graph.write_pdf(\"iris.pdf\")\n",
    "\n",
    "# create png\n",
    "#graph.write_png(\"iris.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.4 Training a Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "randomforest = RandomForestClassifier(random_state=0)\n",
    "model = randomforest.fit(features, target)\n",
    "\n",
    "#randomforest_entropy = RandomForestClassifier(criterion='entropy', random_state=0)\n",
    "#model_entropy = randomforest_entropy.fit(features, target)\n",
    "\n",
    "observation = [[ 5, 4, 3, 2]]\n",
    "model.predict(observation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discussion\n",
    "A common problem with decision trees is that they tend to fit the training data too closely (i.e. overfitting). This has motivated the widespread use of an ensemble learning method called *random forest*. In a random forest, many decision trees are trained, but each tree only recieves a bootstrapped sample of observations (i.e. a random sample of observations with replacement that matches the original number of observations) and each node only considers a subset of features when determining the best split. The forest of randomized decision trees votes to determin the predicted class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.5 Training a Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston()\n",
    "features = boston.data[:, 0:2]\n",
    "target = boston.target\n",
    "\n",
    "randomforest = RandomForestRegressor(random_state=0, n_jobs=-1)\n",
    "model = randomforest.fit(features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.6 Identifying Important Features in Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.feature_importances_: [0.11896532 0.0231668  0.36804744 0.48982043]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "randomforest = RandomForestClassifier(random_state=0)\n",
    "model = randomforest.fit(features, target)\n",
    "\n",
    "importances = model.feature_importances_\n",
    "print(\"model.feature_importances_: {}\".format(importances))\n",
    "indices = np.argsort(importances)[::-1]\n",
    "names = [iris.feature_names[i] for i in indices]\n",
    "plt.figure()\n",
    "plt.title(\"feature importance\")\n",
    "plt.bar(range(features.shape[1]), importances[indices])\n",
    "plt.xticks(range(features.shape[1]), names, rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.7 Selecting Important Features in Random Forests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "randomforest = RandomForestClassifier(random_state=0, n_jobs=-1)\n",
    "\n",
    "# create object that selects features with importance greater\n",
    "# than or requal to a threshold\n",
    "selector = SelectFromModel(randomforest, threshold=0.9)\n",
    "\n",
    "features_important = selector.fit_transform(features, target)\n",
    "\n",
    "model = randomforest.fit(features_important, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### See Also\n",
    "* https://hal.archives-ouvertes.fr/file/index/docid/755489/filename/PRLv4.pdf\n",
    "\n",
    "## 14.8 Handling Imbalanced Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "features = features[40:, :]\n",
    "target = target[40:]\n",
    "\n",
    "target = np.where((target == 0), 0, 1)\n",
    "\n",
    "randomforest = RandomForestClassifier(random_state=0, n_jobs=-1, class_weight=\"balanced\")\n",
    "model = randomforest.fit(features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "A useful argument is `balanced`, wherein classes are automatically weighted inversely proptional to how frequently they appear in the data:\n",
    "$$\n",
    "w_j = \\frac{n}{kn_j}\n",
    "$$\n",
    "where $w_j$ is the weight to class j, n is the number of observations, $n_j$ is the number of observations in class j, and k is the total number of classes.\n",
    "\n",
    "## 14.9 Controlling Tree Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "decisiontree = DecisionTreeClassifier(random_state=0,\n",
    "                                     max_depth=None,\n",
    "                                     min_samples_split=2,\n",
    "                                     min_samples_leaf=1,\n",
    "                                     min_weight_fraction_leaf=0,\n",
    "                                     max_leaf_nodes=None,\n",
    "                                     min_impurity_decrease=0)\n",
    "\n",
    "model = decisiontree.fit(features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.10 Improving Performance Through Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "adaboost = AdaBoostClassifier(random_state=0)\n",
    "\n",
    "model = adaboost.fit(features, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "In random forest, an ensemble (group) of randomized decision trees predicts the target vector. An alternative, and often more powerful, approach is called *boosting*. In one form of boosting called AdaBoost, we iteratively train a series of weak models (most often a shallow decision tree, sometimes called a stupm), each iteration giving higher priority to observations the previous model predicted incorrectly. More specifically in AdaBoost:\n",
    "\n",
    "1. Assign every observation, $x_p$ an initial weight value, $w_i = \\frac{1}{n}$, where n is the total number of observations in the dat\n",
    "\n",
    "2. Train a \"weak\" model on the data\n",
    "\n",
    "3. For each observation:\n",
    "    a. If weak model predicts $x_i$ correctly $w_i$ is increased.\n",
    "    b. If weak model predicts $x_i$ incorrectly $w_i$ is decreased.\n",
    "    \n",
    "4. Train a new weak model where observations with greater $w_i$ are given greater priority.\n",
    "\n",
    "5. Repeat steps 4 and 5 until the data is perfectly predicted or a preset number of weak models has been trained\n",
    "\n",
    "The end result is an aggregated model where individual weak mdoels focus on more difficult (from a prediction perspective) observations.\n",
    "\n",
    "## 14.11 Evaluating Random Forests with Out-of-Bag Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9533333333333334"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "features = iris.data\n",
    "target = iris.target\n",
    "\n",
    "randomforest = RandomForestClassifier(\n",
    "    random_state=0, n_estimators=1000, oob_score=True, n_jobs=-1)\n",
    "\n",
    "model = randomforest.fit(features, target)\n",
    "\n",
    "randomforest.oob_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "In random forests, each decision tree is trained using a boostrapped subset of observations. This means that for every tree there is a separate subset of observations not being used to train that tree. These are called out-of-bag (OOB) observations. We can use OOB observations as a test set to evaluate the performance of our random forest.\n",
    "\n",
    "For every observation, the learning algorithm compares the observation's true vlaue with the prediction from a subset of trees not trained using that observation. The overall score is calculated and provides a single measure of a random forest's performance. OOB score estimation is an alternative to cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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